As businesses mature their AI API usage, they often reach a point where a simple monitoring dashboard is not enough. They need a gateway — a layer between their application and the AI providers that handles routing, fallbacks, caching, rate limiting, and cost management in one place. Portkey and Helicone are the two most popular AI gateway platforms, and while they overlap in some capabilities, they serve different primary use cases. Here is how to choose between them.
What an AI Gateway Does
An AI gateway proxies your API calls and adds a layer of intelligence and control between your application and AI providers. The core capabilities: request routing (send different request types to different models), fallbacks (if the primary model fails or is rate-limited, automatically retry with a backup), caching (return stored responses for identical or similar requests to save cost and reduce latency), observability (log every request with costs and performance metrics), and load balancing (distribute requests across multiple API keys or providers).
Helicone: Monitoring-First, Gateway Second
Helicone started as an observability and monitoring tool and added gateway features over time. Its core strength remains monitoring: the dashboard is clean and intuitive, the cost tracking by custom properties is excellent, and the prompt versioning features are well-designed. The setup is genuinely simple — one line of code to route through Helicone.
Helicone’s gateway features (caching, rate limiting, fallbacks) are functional but less mature than Portkey’s. For teams whose primary need is visibility into AI costs and usage, with basic gateway capabilities as a secondary requirement, Helicone is the simpler and more accessible choice.
Portkey: Gateway-First, with Strong Observability
Portkey was built as a production-grade AI gateway from the start. Its routing and reliability features are more sophisticated: conditional routing based on request content or metadata, automatic fallbacks with configurable retry logic, semantic caching that matches similar (not just identical) requests, and load balancing across multiple provider accounts. The observability features are comprehensive, though the interface is more complex than Helicone’s.
Portkey vs Helicone: Feature Comparison
| Feature | Helicone | Portkey |
|---|---|---|
| Setup simplicity | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Cost monitoring | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Model routing | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Fallback reliability | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| Semantic caching | Limited | ⭐⭐⭐⭐⭐ |
Which to Choose
Choose Helicone if your primary need is monitoring and cost visibility, you want minimal setup time, and your reliability requirements are satisfied by basic fallback capabilities. It is the right choice for most small businesses running moderate-volume AI applications who want better visibility without significant engineering investment.
Choose Portkey if you are running production AI applications where reliability matters, you need sophisticated routing logic to send different request types to different models, or you want semantic caching to reduce costs on similar (not just identical) requests. The additional capability justifies the additional configuration complexity for applications at meaningful scale.
Both tools offer free tiers for testing. Build a small representative workflow on each, compare the dashboard experience and the setup friction, and choose based on which fits your team’s specific needs and technical comfort level.
Setting Up Helicone in Practice
Helicone’s setup is genuinely one-line: change your API base URL from OpenAI’s endpoint to Helicone’s proxy and add your Helicone API key as a header. Every API call routes through Helicone, which logs it and makes it visible in the dashboard. For teams already running OpenAI API calls, this change takes under five minutes and requires no other code modifications. The immediate benefit is visibility: every call is logged with token counts, cost, latency, and the full request and response. For teams that have never measured their AI API costs at a request level, this alone is valuable — you will likely find cost patterns you did not know existed.
Helicone’s custom property feature is worth configuring early. Adding a header like Helicone-Property-Team or Helicone-Property-Workflow to each API call enables per-team and per-workflow cost reporting in the dashboard. This granular visibility is the foundation of any meaningful AI cost management practice.
Setting Up Portkey for Production Use
Portkey’s setup involves creating a configuration that defines your routing rules, fallback chains, and caching settings. The configuration is stored in Portkey and referenced by a config ID in your API calls, meaning changes to routing logic can be made in the Portkey dashboard without redeploying your application. This separation of routing configuration from application code is particularly valuable in production environments where routing changes need to happen quickly in response to provider outages or pricing changes.
Portkey’s virtual keys abstract your actual API keys behind Portkey-managed identifiers. Your application stores a Portkey virtual key rather than a raw OpenAI or Anthropic API key — if a key needs to be rotated, you update it in Portkey once rather than in every application that uses it. For teams managing multiple provider accounts or multiple environments, this key management is a meaningful operational improvement beyond the gateway and observability features.
When to Switch Tools
Many teams start with Helicone for its simplicity and upgrade to Portkey when their requirements outgrow it. The upgrade trigger is usually one of: needing sophisticated fallback logic for a production application where provider outages are unacceptable, needing semantic caching to reduce costs on a high-volume conversational application, or needing conditional routing to send different request types to different models based on content characteristics. If you find yourself working around Helicone’s limitations rather than just using its features, evaluate Portkey — the migration is straightforward since both use the same OpenAI-compatible proxy pattern.
Set up Helicone this week on your highest-volume AI workflow. The visibility it provides into costs and performance is immediately useful, and the upgrade path to Portkey is clear if your requirements grow beyond what Helicone covers.
Portkey’s Semantic Caching in Practice
Portkey’s semantic caching is one of its most distinctive capabilities — and one of the most valuable for conversational AI applications. Unlike exact-match caching that only returns cached results for byte-identical queries, semantic caching uses embedding similarity to match queries that mean the same thing even when phrased differently. “How do I reset my password?” and “Where can I change my password?” produce similar embeddings and return the same cached answer. For customer service applications, knowledge base Q&A, and any application where users ask similar questions in varied phrasings, semantic caching can reduce API costs by 20–40% on top of any other optimisations in place.
Gateway Selection for Production Systems
The right gateway for a production system is the one that fails gracefully when things go wrong. Evaluate each gateway’s behaviour under failure conditions: what happens when the primary AI provider returns an error — does the gateway fail open (pass the error to your application), fail closed (return a cached fallback), or automatically retry with a configured backup provider? What happens if the gateway itself is unavailable — can your application fall back to direct API calls? These failure mode questions are more important for production reliability than feature comparisons, because features only matter when the system is working and failures only matter when it is not.
Start with Helicone for its simplicity, learn from the visibility it provides, and evaluate Portkey when your requirements outgrow what Helicone’s gateway features support. The most important step is getting observability in place — the tool you use for that is secondary to the practice of actually monitoring and managing your AI infrastructure.
Security Practices for AI Gateway Credentials
AI gateways sit between your applications and your AI providers, which means they require careful credential management. The gateway itself needs your provider API keys to make calls on your behalf — those keys should be stored as environment variables or in a secrets manager, never hardcoded in configuration files. Your applications connect to the gateway using gateway-specific virtual keys that do not expose your underlying provider credentials — this separation means that a compromised application does not expose your provider keys, and that rotating a provider key requires only updating it in the gateway rather than in every application. Implement IP allowlisting for your gateway endpoint where your architecture permits it, adding a network-level control to the credential-level control. These security practices are foundational for any production AI gateway deployment.
The businesses that build genuine AI capability over time are those that treat each deployment as a learning opportunity — measuring what works, understanding what does not, and applying those lessons to the next implementation. That iterative discipline, applied consistently across your AI portfolio, produces compounding improvements in quality, reliability, and business impact that no single optimal deployment decision can match.